JetBrains Research unites scientists working in challenging new disciplines

Deep Multi-Agent Reinforcement Learning with Relevance Graphs

25 November 2018

At this seminar, we will discuss a novel approach, called MAGnet, to multi-agent reinforcement learning (MARL) that utilizes a relevance graph representation of the environment obtained by a self-attention mechanism, and a message-generation technique inspired by the NerveNet architecture.

MAGnet approach was applied to the Pommerman game and the results show that it significantly outperforms state-of-the-art MARL solutions, including DQN, MCTSNet, and MADDPG.